Infrastructure control and GPU orchestration
Manage GPU clusters across on-premises, cloud, or hybrid environments with scheduling, quota management, dynamic fractional GPUs, and usage-based billing controls.
ClearML
ClaimClearML is an AI infrastructure platform for managing GPU clusters, building and training AI/ML models, and deploying GenAI workloads in hosted, self-hosted, and hybrid environments.
What ClearML is
ClearML is an AI infrastructure platform built around three connected layers: an Infrastructure Control Plane, an AI Development Center, and a GenAI App Engine. Together they are positioned to help teams manage compute, build and train AI/ML models, and deploy GenAI applications across enterprise environments.
The platform is designed for organizations that need both software workflow control and infrastructure control. Source pages describe support for GPU cluster management, experiment tracking, pipelines, model repositories, CI/CD automation, dataset versioning, and LLM deployment, with deployment options that include hosted, self-hosted, VPC, on-premises, air-gapped, and hybrid setups.
Core capabilities
End-to-end AI/ML development workspace
Use the AI Development Center to manage experiments, datasets, models, artifacts, metrics, reports, and pipelines from a shared workbench.
Reproducible experiment tracking
Track code, configuration, logs, packages, and uncommitted changes to keep model runs reproducible and easier to debug.
Dataset versioning and data management
Create reusable dataset versions with searchable metadata, previews, inheritance, differential storage, and flexible storage backends such as HTTP, S3, GCS, Azure, and NAS.
GenAI deployment workflow
Deploy and iterate on GenAI workloads with App Engine tooling for LLM APIs, data ingestion, vector database creation, and feedback gathering.
Flexible deployment model
Fit the platform to different operating models with hosted, self-hosted, VPC, on-premises, air-gapped, or hybrid deployment paths.
Common ways teams use ClearML
Centralized AI infrastructure operations
Provision and govern GPU capacity across teams, then allocate work with quota management, scheduling, and fractional GPU controls.
Model development and experimentation
Track experiments, compare runs, manage datasets, and keep model artifacts organized during iterative AI/ML development.
Training orchestration at scale
Queue training jobs and let the platform manage scheduling, containerized execution, and reproducibility across on-prem, cloud, or HPC resources.
GenAI application rollout
Deploy custom or fine-tuned LLMs, configure access controls, and support model iteration with data ingestion and vector database tooling.
Workflow automation for AI teams
Use a single platform for versioning, reporting, and CI/CD automation when teams need tighter handoff between development and production.
Pros and Cons
Pros
- Combines infrastructure management, AI development, and GenAI deployment in one platform.
- Supports multiple deployment modes, including hosted, self-hosted, VPC, on-premises, air-gapped, and hybrid options.
- Includes practical MLOps features such as experiment tracking, pipelines, model versioning, dataset versioning, and CI/CD automation.
- Provides workload management features for GPU scheduling, quota control, and dynamic fractional GPUs.
- Offers a clear free Community tier alongside paid and quote-based plans.
Cons
- The source pages do not provide a full public integration list, so specific third-party compatibility is not fully documented here.
- Some capabilities are split across different plan tiers, and advanced infrastructure features are reserved for higher tiers or custom quotes.
FAQ
How does ClearML split its platform across different layers?
ClearML’s platform is presented as a three-layer system: Infrastructure Control Plane, AI Development Center, and GenAI App Engine. The Development Center is the workbench for building, training, and deploying AI/ML models, while the other layers handle infrastructure and GenAI deployment.
What deployment options does ClearML support?
The pricing page says ClearML is available on hosted servers, self-hosted, or as a managed service, with deployment options including VPC, on-premises, air-gapped, or hybrid setups depending on the plan.
What can teams do in the AI Development Center?
The source material says the AI Development Center supports experiment management, orchestration, dashboards, pipelines, model repository, CI/CD integration, data integration, hyperparameter optimization, and model deployment. It also notes support for model fine-tuning and vector database integration in the pricing feature matrix.
What pricing plans are available?
The pricing page lists Community, Pro, Scale, and Enterprise options. Community is shown as free, Pro is a paid per-user plan, and Scale and Enterprise use custom quotes.
Can ClearML be self-hosted?
Yes. The home page says ClearML is a unified, open source platform, and the pricing page notes that you can also run self-hosted ClearML as 100% open source on GitHub.
Quick Facts
- Category
- AI Infrastructure Platform
- Primary users
- AI builders, AI/ML teams, and IT/DevOps teams
- Core workflow
- Manage compute, develop models, and deploy GenAI from one platform
- Deployment
- Hosted, self-hosted, VPC, on-premises, air-gapped, and hybrid
- Pricing shape
- Free Community plan, paid Pro plan, and custom-quote Scale and Enterprise plans
- Website
- clear.ml